Covid Report for Family

Background

Notes and format last updated May 7, 2020

Starting on the May 7th update, the NY Times began including probable covid cases/deaths along with confirmed. This mostly affects death counts – for certain geographies that include probable COVID deaths in addition to confirmed, these are now added to the totals. For the time being, they were all added to the May 6th totals, causing a big spike at the U.S. level. Over time, NY Times will revise their historical counts and distribute these added deaths when they actually occurred, so the spike should fade.

• This page shows COVID data broken down into regions that affect our family. It pulls from publicly available raw data from the NY Times, which is reported daily. It’s on a one-day lag, so when you see this report, the most recent day included will be that reported the day prior.
• The table of contents above summarizes the major sections and can be used to jump to those sections.
• Each major section is then divided into tabs to allow you to more easily shuffle between geographies and metrics.
  • If you open on your phone, I don’t believe the tabs will work, so you’ll just see the page as one long, continuous document.
• More detailed descriptions and context can be found in each section.

Growth rates

Heat maps

• The two heat maps below compare how quickly total cases or deaths have grown at various times in our respective geopgraphies.
• The first plot compares growth rate for total cases; the second, growth rate for total deaths.
• The metric used is doubling time, by which I mean how quickly total cases or deaths are doubling.
• The plots track that doubling time at each date for our geographies. Darker colors reflect shorter doubling times, and thus periods of faster growth.
  • You can use the plots to track each geography over time and to compare the geographies to one another.
  • You can also compare the cases and death charts, to see how faster periods of death growth follow faster periods of case growth.

Case growth rates

• This section charts the growth rate of both total and new cases for each of our respective geographies. Each geography has its own chart, and then that chart will have a trendline for total cases and new cases.
  • There are only plots for the U.S. and states because the numbers for the counties are too small to generate worthwhile trendlines in this section.
• Note that we’re charting growth rate and not a count of cases, so don’t think of these as the standard “curve” that we hear about in the news and that we want to flatten. Instead, these growth rate charts help track more precisely what we can only estimate when we see those other curves. For these growth rate charts, if the line is above zero, the metric we are tracking (total or new cases) is continuing to grow. If the growth rate line is going up, it’s growing more quickly each day; if it’s going down but still above zero, it’s growing less quickly (but still growing). Only when the growth rate lines go below zero has the metric stopped growing.
• Each of these two lines uses rolling windows to calculate a growth rate for that particular metric. I do the calculation differently for each to smooth out some of the large day-to-day discrepancies in new case reporting at the state level.
  • For total cases, the trendlines are a rolling 3-day average of daily growth rates in total cases. We want to see these decline (and almost all are), but they can’t go below zero. This is because we’re tracking growth rate and a growth rate line below zero would mean total cases have gone down, which can’t happen. They can only grow less quickly, which means we want to see the total case line get as close to zero as possible.
  • For new cases, the trendlines show a rolling 3-day average of daily growth rate in the rolling 7-day average of new cases. Including two rolling periods in this average helps smooth out crazy spikes at the state level that result from large day-to-day changes. Unlike the lines for total cases, we want to watch for the lines for new cases to get consistently below zero and stay there. That means that we are consistently seeing fewer new cases on a daily basis.

U.S.

Our states

Death growth rates

• This section charts the growth rate of both total and new deaths for each of our respective geographies. Each geography has its own chart, and then that chart will have a trendline for total deaths and new deaths.
  • There are only plots for the U.S. and states because the numbers for the counties are too small to generate worthwhile trendlines in this section.
• Note that we’re charting growth rate and not a count of deaths, so don’t think of these as the standard “curve” that we hear about in the news and that we want to flatten. Instead, these growth rate charts help track more precisely what we can only estimate when we see those other curves. For these growth rate charts, if the line is above zero, the metric we are tracking (total or new deaths) is continuing to grow. If the growth rate line is going up, it’s growing more quickly each day; if it’s going down but still above zero, it’s growing less quickly (but still growing). Only when the growth rate lines go below zero has the metric stopped growing.
• Each of these two lines uses rolling windows to calculate a growth rate for that particular metric. I do the calculation differently for each to smooth out some of the large day-to-day discrepancies in new death reporting at the state level.
  • For total deaths, the trendlines are a rolling 3-day average of daily growth rates in total deaths. We want to see these decline (and almost all are), but they can’t go below zero. This is because we’re tracking growth rate and a growth rate line below zero would mean total deaths have gone down, which can’t happen. They can only grow less quickly, which means we want to see the total death line get as close to zero as possible.
  • For new deaths, the trendlines show a rolling 3-day average of daily growth rate in the rolling 7-day average of new deaths. Including two rolling periods in this average helps smooth out crazy spikes at the state level that result from large day-to-day changes. Unlike the lines for total deaths, we want to watch for the lines for new deaths to get consistently below zero and stay there. That means that we are consistently seeing fewer new deaths on a daily basis.

U.S.

Our states

By population rankings

This section tracks metrics for states and counties normalized for population (number of cases or deaths per million residents), and then compares these figures both for our geographies and the country overall.

States

• This section shows tables ranking all 50 states for per populations rates of total cases, new cases, total deaths, and new deaths.
• For each metric, in addition to the tables, the trends for the top states are plotted over time.
  • We only plot the top ten states for each metric so that the plots aren’t too crowded. But you can view the full 50-state rankings in the tables.

Total confirmed cases

Table of total confirmed cases per million residents (all 50 states)

Ranking	State	Cases Per Million
1	Louisiana	35,775
2	Florida	32,652
3	Mississippi	32,608
4	Alabama	31,200
5	Arizona	29,883
6	South Carolina	28,444
7	Georgia	28,157
8	Tennessee	27,966
9	Iowa	27,655
10	North Dakota	27,538
11	Arkansas	27,188
12	Texas	26,682
13	Nevada	25,749
14	South Dakota	24,572
15	New York	23,706
16	New Jersey	23,180
17	Idaho	23,158
18	Illinois	23,135
19	Rhode Island	23,055
20	Nebraska	23,044
21	Utah	22,284
22	District of Columbia	21,628
23	Oklahoma	21,530
24	Wisconsin	21,351
25	Delaware	20,938
26	Missouri	20,759
27	California	20,621
28	Maryland	20,571
29	Kansas	20,513
30	North Carolina	19,879
31	Massachusetts	19,016
32	Indiana	17,911
33	Minnesota	17,322
34	Virginia	17,174
35	Connecticut	16,028
36	Kentucky	15,892
37	Puerto Rico	14,848
38	New Mexico	13,823
39	Michigan	13,596
40	Ohio	12,986
41	Pennsylvania	12,626
42	Colorado	12,149
43	Washington	11,897
44	Montana	11,731
45	Alaska	11,587
46	Wyoming	9,941
47	Hawaii	8,723
48	West Virginia	8,655
49	Oregon	7,822
50	New Hampshire	6,036
51	Maine	3,942
52	Vermont	2,796

New confirmed cases

Table of new cases per million residents: rolling 3-day average (all 50 states)

Ranking	State	New Cases Per Million
1	North Dakota	480
2	South Dakota	446
3	Wisconsin	398
4	Utah	302
5	Montana	275
6	Puerto Rico	262
7	Nebraska	244
8	Iowa	235
9	Arkansas	232
10	Missouri	230
11	Oklahoma	225
12	Kansas	223
13	Idaho	215
14	Tennessee	206
15	Minnesota	203
16	Wyoming	192
17	South Carolina	165
18	Alabama	158
19	Alaska	156
20	Illinois	152
21	Nevada	149
22	Indiana	144
23	Kentucky	138
24	Texas	138
25	North Carolina	122
26	Mississippi	113
27	Delaware	104
28	West Virginia	103
29	Georgia	90
30	Colorado	84
31	Florida	84
32	Virginia	84
33	Louisiana	83
34	Maryland	83
35	Ohio	82
36	New Mexico	79
37	Michigan	78
38	California	76
39	Massachusetts	76
40	Rhode Island	76
41	New Jersey	75
42	Hawaii	73
43	Pennsylvania	67
44	Washington	63
45	Arizona	52
46	Connecticut	52
47	Oregon	52
48	District of Columbia	47
49	New York	46
50	New Hampshire	30
51	Maine	16
52	Vermont	7

Total deaths

Table of total deaths per million residents (all 50 states)

Ranking	State	Deaths Per Million
1	New Jersey	1,813
2	New York	1,682
3	Massachusetts	1,365
4	Connecticut	1,263
5	Louisiana	1,178
6	Rhode Island	1,047
7	Mississippi	981
8	District of Columbia	884
9	Arizona	772
10	Michigan	706
11	Illinois	700
12	Florida	653
13	Delaware	651
14	Maryland	651
15	South Carolina	648
16	Georgia	640
17	Pennsylvania	639
18	Texas	548
19	Indiana	533
20	Nevada	514
21	Alabama	510
22	Arkansas	440
23	Iowa	419
24	New Mexico	416
25	Ohio	406
26	California	395
27	Virginia	371
28	Minnesota	366
29	Colorado	356
30	Missouri	345
31	Tennessee	345
32	North Carolina	330
33	New Hampshire	322
34	North Dakota	310
35	Washington	288
36	Kentucky	267
37	Idaho	257
38	Oklahoma	254
39	Nebraska	251
40	South Dakota	246
41	Wisconsin	222
42	Kansas	220
43	Puerto Rico	202
44	West Virginia	188
45	Montana	162
46	Utah	141
47	Oregon	130
48	Maine	104
49	Hawaii	92
50	Vermont	92
51	Wyoming	86
52	Alaska	71

New deaths

Table of new deaths per million residents: rolling 3-day average (all 50 states)

Ranking	State	New Deaths Per Million
1	Arkansas	6
2	North Dakota	6
3	Mississippi	3
4	Texas	3
5	Georgia	2
6	Louisiana	2
7	Massachusetts	2
8	Missouri	2
9	South Carolina	2
10	Alaska	1
11	Arizona	1
12	Delaware	1
13	Florida	1
14	Hawaii	1
15	Illinois	1
16	Indiana	1
17	Iowa	1
18	Kentucky	1
19	Maryland	1
20	Minnesota	1
21	Montana	1
22	Nevada	1
23	New Mexico	1
24	North Carolina	1
25	Oklahoma	1
26	Puerto Rico	1
27	Tennessee	1
28	Virginia	1
29	West Virginia	1
30	Alabama	0
31	California	0
32	Colorado	0
33	Connecticut	0
34	District of Columbia	0
35	Idaho	0
36	Kansas	0
37	Maine	0
38	Michigan	0
39	Nebraska	0
40	New Hampshire	0
41	New Jersey	0
42	New York	0
43	Ohio	0
44	Oregon	0
45	Pennsylvania	0
46	Rhode Island	0
47	South Dakota	0
48	Utah	0
49	Vermont	0
50	Washington	0
51	Wisconsin	0
52	Wyoming	0

Counties

• This section focuses on the county level. It shows tables with our counties ranked by percentile of U.S. counties for per population rates of total cases and total deaths.
  • Each table also shows the top five counties in the country in addition to our counties, for added perspecive.
• In addition to the tables, our counties’ percentile for both total cases and total deaths are plotted over time.

Confirmed cases

Table showing total cases per million and percentile for all US counties. Includes our counties and the top 5 in the US for perspective.

County	State	Cases Per Million	Raw Ranking	Percentile
Lincoln	Arkansas	155,022	1	99
Chattahoochee	Georgia	149,812	2	99
Trousdale	Tennessee	147,820	3	99
Lafayette	Florida	146,402	4	99
Lake	Tennessee	131,842	5	99
Davidson	Tennessee	41,556	195	93
Richland	South Carolina	36,896	266	91
York	South Carolina	19,176	1172	62
Orange	California	17,291	1352	56
Pierce	Washington	9,785	2215	29

Our county percentiles over time

Deaths

Table showing total deaths per million and percentile for all US counties. Includes our counties and the top 5 in the US for perspective.

County	State	Deaths Per Million	Raw Ranking	Percentile
Hancock	Georgia	4,966	1	99
Kenedy	Texas	4,950	2	99
Emporia city	Virginia	4,863	3	99
Galax city	Virginia	4,727	4	99
Randolph	Georgia	4,279	5	99
Richland	South Carolina	587	685	78
Davidson	Tennessee	442	975	68
Orange	California	383	1114	64
York	South Carolina	267	1447	53
Pierce	Washington	246	1508	52

Our county percentiles over time

Raw counts

• This section shows the raw counts over time for the U.S., our states, and our counties for four categories:
  • Total confirmed cases: This is the cumulative total of cases reported.
  • New confirmed cases: This is the number of new cases reported on each respective date.
    • For the U.S. and states, a trendline shows the rolling 7-day average of new cases, to help better visualize the peak.
  • Total deaths: This is the cumulative total of deaths reported.
  • New deaths: This is the number of new deaths reported on each respective date.
    • For the U.S. and states, a trendline shows the rolling 7-day average of new deaths, to help better visualize the peak.

Total confirmed cases

U.S.

Our states

Our counties

New confirmed cases

U.S.

Our states

Our counties

Total deaths

U.S.

Our states

Our counties

New deaths

U.S.

Our states

Our counties

Stay-at-home comparisons

• This section is a work in progress. It’s exploring what effect stay-at-home orders have on spread. It’s inherentely messy because some states have localized orders but not orders for their full state and because states implemented the orders at different times.
  • The first plot shows a rolling 3-day average of new cases per million residents, averaged across 2 groups of states – those with a state-wide stay-at-home order in effect and those without. The size of the dots reflect the number of states in each group for each given day (so the dots are getting bigger over time for the group with orders, as more states implement those orders).
    • So far, this view doesn’t show if these orders are “working” or not. Instead, the biggest takeaway from the current plot is that the states that implemented orders are reacting to having worse spread, which is why the case rate is so much higher at the start for them. As a state decides it has a problem and implements an order, it joins the grouping of states with orders and drives the rate up for that group.
    • Over time (if the policies work), what we’d expect is that the line for the group with orders flattens or declines in relation to the no-order group. It also may become moot if all states issue orders.
    • As states start rescending their orders, I’ll put them back into the no-order group, so the no-order group should beging to expand again.
  • The second plot tracks progress of states by the number of days since orders have been in effect. It includes only those states with orders and shows their average new case growth rate (a rolling 3-day average of daily growth rates) based on how long it’s been since the order went into effect. Again, the size of the dots show how many states are included in each point – i.e., as you go further right, the dots are smaller because less states are that far past their order.
    • I’m working on how to add a baseline to this chart to compare to states without orders, but that’s challenging because there’s no way to fully align timing.